Particle Detection Methods and Systems for Practicing Same

ABSTRACT

Aspects of the present disclosure include methods for detecting events in a flow cytometer. Also provided are methods of detecting cells in a flow cytometer. Other aspects of the present disclosure include methods for determining a level of contamination in a flow cell. Computer-readable media and systems, e.g., for practicing the methods summarized above, are also provided.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional PatentApplication No. 62/269,294 filed Dec. 18, 2015, which application isincorporated herein by reference in its entirety.

INTRODUCTION

A variety of methods are used for cellular analysis, including visualand/or automated inspection via light or fluorescent light microscopy.Cellular examinations and analyses of these types are commonly practicedin order to obtain information regarding cell lineage, maturationalstage, and/or cell counts in a sample.

Flow cytometry is a method for identifying and distinguishing betweendifferent cell types in a non-homogeneous sample. In the flow cytometer,cells are passed one at a time or nearly one at a time through a sensingregion where each cell is irradiated by an energy source. Typically,single wavelength light sources (e.g., lasers, etc.) are used as theenergy source and one or more of a variety of sensors record data basedon the interaction of the cells with the applied energy. Flow cytometryis commonly used in hematology and has been successful in the diagnosisof blood diseases, including blood cancers. In addition to flowcytometry, other analytical methods are used in hematology and incharacterizing a population of cells.

Challenges in flow cytometry include the capture of noise-free particleevents (e.g., cell events). For example, optical side-lobes, baselinedrift, fluidics drift and/or the electronics baseline time constantproduce undesirable signals that resemble small cell events. Otherfactors that adversely affect the quality of flow cytometry data includevariation in nucleated cell counts from sample to sample, e.g., whichresults in variation in fluorescent signals such that the cell eventsoccupy very little dynamic range of the analog to digital converter(ADC). In addition, flow cell contamination may prevent a flow cytometerfrom producing valid clinical results.

SUMMARY

Aspects of the present disclosure include methods for detecting eventsin a flow cytometer. Such methods include flowing particles through aflow cell of a flow cytometer, optically interrogating the particlesflowing through the flow cell, extracting putative event features, andtime-stamping putative events. Such methods further include determininga time difference between a putative previous event and a putativecurrent event and comparing the time difference to a threshold duration.If the time difference is greater than the threshold duration, theputative current event is stored as a current event. If the timedifference is less than the threshold duration, a peak height feature ofthe putative current event and a peak height feature of the putativeprevious event are compared to a threshold peak height. If the peakheight feature of the putative current event is less than the thresholdpeak height, the putative current event is discarded. If the peak heightfeature of the putative previous event is less than the threshold peakheight, the putative previous event is discarded. If the peak heightfeature of the putative previous event is greater than the thresholdpeak height, the putative previous event is stored as a previous event.

Also provided are methods of detecting cells in a flow cytometer. Suchmethods include flowing a cellular sample through a flow cell of a flowcytometer, detecting optical signals from the cells flowing through theflow cell at a first gain setting, and detecting optical signals fromthe cells flowing through the flow cell at the second gain setting. Thesecond gain setting is different from the first gain setting.

Other aspects of the present disclosure include methods for determininga level of contamination in a flow cell. Such methods include flowing asample including particles through a flow cell, collecting raw particleevent data during the flowing, counting the number of particle eventswithin the raw particle event data and filtering the raw particle eventdata with an inverse Gaussian filter coefficient to produce a filteredsignal. The inverse Gaussian filter coefficient is based on an expectedpower spectrum variance. Such methods further include determining theenergy of the filtered signal, and subtracting a clean flow cellbaseline energy from the determined energy of the filtered signal, todetermine a level of contamination in the flow cell.

Computer-readable media and systems, e.g., for practicing the methods ofthe present disclosure, are also provided.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1, panels A and B, depict how a cell particle travels within anilluminated flow cell and the plotted convolution response,respectively.

FIG. 2 depicts a plot showing an ideal “clean” Gaussian response.

FIG. 3 depicts a plot showing baseline drift and undesirable cell eventresponse.

FIG. 4 is a flowchart for removing invalid cell event signals accordingto one embodiment of the present disclosure.

FIG. 5 depicts a plot showing contamination of platelet count andconcentration by artifacts.

FIG. 6 depicts a plot showing the elimination of undesirable events, asachieved using a method according to one embodiment of the presentdisclosure.

FIG. 7 depicts a plot showing RBC and PLT events.

FIG. 8 depicts a zoomed-in section of the plot in FIG. 7.

FIG. 9 depicts switching from a PLT collection gain setting to an RBCcollection gain setting during the course of a single assay.

FIG. 10 depicts plots that show counts performed under a fixed gainsetting during the entire assay.

FIG. 11 depicts plots that show counts performed with dynamic gainadjustment during the assay.

FIG. 12 is a flow chart for determining a level of contamination in aflow cell according to one embodiment of the present disclosure.

FIG. 13 depicts the power spectrum of the ideal Gaussian pulse.

FIG. 14 depicts the power spectrum obtained from a flow cytometer.

FIG. 15 depicts the frequency response of an inverse Gaussian filter.

FIG. 16 depicts the time-domain signal for a highly-contaminated flowcell, in which baseline noise was removed from the raw data from an ADC.

FIG. 17 depicts the frequency domain characteristic of thehighly-contaminated flow cell (power spectrum of the raw data).

FIG. 18 depicts the power spectrum of the raw data after removing theenergy equivalent to a clean flow cell from the raw data of thehighly-contaminated flow cell.

FIG. 19 depicts the time-domain signal for a flow cell at an early stageof contamination, in which baseline noise was removed from the raw datafrom an ADC.

FIG. 20 depicts the frequency domain characteristic of the flow cell atan early stage of contamination (power spectrum of the raw data).

FIG. 21 depicts the power spectrum of the raw data after removing theenergy equivalent to a clean flow cell from the raw data of the flowcell at an early stage of contamination.

FIG. 22 depicts the time-domain signal for a flow cell that is notcontaminated, in which baseline noise was removed from the raw data froman ADC.

FIG. 23 depicts the frequency domain characteristic of the flow cellthat is not contaminated (power spectrum of the raw data).

FIG. 24 depicts the power spectrum of the raw data after removing theenergy equivalent to a clean flow cell from the raw data of the flowcell that is not contaminated.

FIG. 25 is a schematic illustration of an example flow cytometeraccording to one embodiment which finds use in implementing the methodsof the present disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure include methods for detecting eventsin a flow cytometer. Also provided are methods of detecting cells in aflow cytometer. Other aspects of the present disclosure include methodsfor determining a level of contamination in a flow cell.Computer-readable media and systems, e.g., for practicing the methodssummarized above, are also provided.

Before the present methods, computer-readable media, and systems aredescribed in greater detail, it is to be understood that the presentdisclosure is not limited to particular embodiments described, as suchmay, of course, vary. It is also to be understood that the terminologyused herein is for the purpose of describing particular embodimentsonly, and is not intended to be limiting.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the present methods,computer-readable media, and systems. The upper and lower limits ofthese smaller ranges may independently be included in the smaller rangesand are also encompassed within the methods, computer-readable media,and systems, subject to any specifically excluded limit in the statedrange. Where the stated range includes one or both of the limits, rangesexcluding either or both of those included limits are also included inthe methods, computer-readable media, and systems.

Certain ranges are presented herein with numerical values being precededby the term “about.” The term “about” is used herein to provide literalsupport for the exact number that it precedes, as well as a number thatis near to or approximately the number that the term precedes. Indetermining whether a number is near to or approximately a specificallyrecited number, the near or approximating un-recited number may be anumber which, in the context in which it is presented, provides thesubstantial equivalent of the specifically recited number.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, representativeillustrative methods, computer-readable media, and systems are nowdescribed.

All publications and patents cited in this specification are hereinincorporated by reference as if each individual publication or patentwere specifically and individually indicated to be incorporated byreference and are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited. The citation of any publication is for itsdisclosure prior to the filing date and should not be construed as anadmission that the present invention is not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dateswhich may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise. It is further noted that the claimsmay be drafted to exclude any optional element. As such, this statementis intended to serve as antecedent basis for use of such exclusiveterminology as “solely,” “only” and the like in connection with therecitation of claim elements, or use of a “negative” limitation.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentmethods, computer-readable media, and systems. Any recited method can becarried out in the order of events recited or in any other order whichis logically possible.

Methods

As summarized above, aspects of the present disclosure include methodsfor detecting events in a flow cytometer. Capturing noise-free cellevents is made difficult by phenomena such as optical side-lobes,fluidics drift, baseline drift, flow cell contamination, and the like.These phenomena produce undesirable signals that resemble small cellevents. These undesirable signals are often generated before and/orafter a larger valid signal has been generated. These erroneouslycaptured undesirable signals contaminate the feature extraction of validcell events, e.g., small cell events.

Cell particles travel within an illuminated flow cell as depicted inFIG. 1, Panel A. During the time a cell particle travels within theilluminated volume, a photo detector captures the response of theconvolution of the laser beam profile and cell particle. The convolutionresponse is shown in FIG. 1, Panel B.

$\begin{matrix}{{{CellEvent}(t)} \equiv {{{CellParticle}(t)}*{{laserprofile}(t)}}} \\{= {\int\limits_{0}^{t}{{{CellParticle}(t)}{{laserprofile}\left( {t - \tau} \right)}{dt}}}}\end{matrix}\quad$

A digital data capture system can digitize this cell event response,e.g., at a rate of 20 mega samples per second. The width of the cellevent response depends on the velocity of the stream and height of thelaser beam profile. This cell event response generates an approximatelyGaussian waveform which can be characterized as:

${{CellEvent}(t)} = {\frac{1}{\sigma\sqrt{2\pi}}e^{\frac{- {({t - u})}^{2}}{2\sigma^{2}}}}$

The parameter μ is a mean of the standard distribution and σ is thestandard deviation (while the normal statistical distribution,characterized by a mean and standard deviation, is Gaussian in nature,the Gaussian signal pulse is not statistical). The variance (σ²) of thisdistribution depends on the stream velocity in the flow cell. As long asthe sheath pressure (and hence the stream velocity) is nearly constantin the flow cell, pulse width (duration at 1/e2 points) of thedistribution is nearly constant.

During hematology analysis, platelets (PLT) are the smallest types ofthe cells to detect in the presence of larger reticulocytes and whiteblood cells. In order to accurately count platelets, the platelets mustbe distinguished from undesired noise-like cell events. Opticalside-lobes, fluidics baseline drift, and the like produce undesirablesignals which resemble valid cell event responses. The time proximity ofthe larger cell event negatively affects the valid small cell (e.g.,platelet) event histogram. The present inventors have found that theaddition of a high resolution time-stamp—added while extracting featuresof every cell event—may be exploited to detect and remove the artifactsgenerated by optical side-lobes, fluidics baseline drift, and the like.The inventors have found that it is possible to isolate small amplitudeevents in the proximity of the larger cell events by employing a highresolution time-stamp for each extracted cell event. Feature extractionaccording to the methods of the present disclosure enables determinationfrom the pulse width whether proximity events are valid cell (e.g.,platelet) events or signals derived from optical system side-lobes,fluidics drift, baseline drift, flow cell contamination, and/or thelike.

According to certain embodiments, the methods for detecting events in aflow cytometer include flowing particles through a flow cell of the flowcytometer, optically interrogating the particles flowing through theflow cell, extracting putative event features, and time-stampingputative events. Such methods further include determining a timedifference between a putative previous event and a putative currentevent and comparing the time difference to a threshold duration. If thetime difference is greater than the threshold duration, the putativecurrent event is stored as a current event. If the time difference isless than the threshold duration, a peak height feature of the putativecurrent event and a peak height feature of the putative previous eventare compared to a threshold peak height. If the peak height feature ofthe putative current event is less than the threshold peak height, theputative current event is discarded. If the peak height feature of theputative previous event is less than the threshold peak height, theputative previous event is discarded. If the peak height feature of theputative previous event is greater than the threshold peak height, theputative previous event is stored as a previous event.

By “event” is meant the passing of a particle (e.g., a cell) through aninterrogation zone of the flow cell, as detected by an opticalinterrogation system. By “putative event” is meant optical signalfeatures resembling an event which may or may not be produced by anevent. The methods of the present disclosure enable the determination ofwhether a putative event is indeed an event, or whether the putativeevent is derived from signals which resemble valid cell event responsesbut are not in fact valid cell event responses, e.g., signals arisingfrom optical side-lobes, fluidics baseline drift, and the like. As such,in certain aspects of the subject methods, an event is distinguishedfrom a signal selected from optical system side-lobes, fluidics drift,baseline drift, flow cell contamination, and combinations thereof.

Flow stream velocity is a function of sheath pressure, fluid viscosityand flow cell dimensions. In certain aspects, flowing cells through theflow cell includes flowing the cells at a sheath pressure of 9 psi orgreater, e.g., 9 psi, 10 psi or greater, 11 psi or greater, 12 psi orgreater, 13 psi or greater, 14 psi or greater, 15 psi or greater, etc.According to certain embodiments, the beam height of the laser is 5 μmor greater, e.g., 5 μm, 6 μm or greater, 7 μm or greater, 8 μm orgreater, 9 μm or greater, 10 μm or greater, 15 μm or greater, or 20 μmor greater.

Particles (e.g., cells) convolve with laser beam height to generateevent profiles. When the sheath pressure is about 12 psi and the laserbeam height is about 8 μm, this event profile divided by flow-streamvelocity produces approximately 2 μs wide Gaussian pulses. An ideal(“clean”) Gaussian response is depicted in FIG. 2. Under idealconditions, there are no undesirable signals near the main Gaussianparticle profile. However, in practice, phenomena such as optical sidelobes, fluidics drift, baseline drift, and the like, produce undesirablesignals near the main Gaussian particle profile. As an example, FIG. 3shows a signal near the main Gaussian particle profile arising frombaseline drift. The present inventors have found that, by processing theparticle feature extraction according to the methods of the presentdisclosure, it is possible to determine whether a putative event is anevent (e.g., the passing of a cell (e.g., a platelet) through the flowcell) or a non-event.

According to certain embodiments, the threshold duration is 2.5 μs orless, e.g., 2 μs. In certain aspects, comparing a peak height feature ofthe putative current particle event and a peak height feature of theputative previous particle event to a threshold peak height includesdetermining whether the peak heights of the putative current cell eventand putative previous cell event are less than 2× a threshold peakheight.

In certain aspects, the particles are microparticles, such asmicroparticles having fluorescent moieties incorporated therein, orhaving fluorescent moieties attached directly or indirectly to thesurface thereof. By “microparticle” is meant a particle (which is not acell), having a greatest dimension ranging from 0.001 μm to 1000 μm,such as from 0.5 μm to 100 μm, e.g., 0.1 μm to 20 μm. In certainaspects, the microparticle has a greatest dimension of 20 μm or less,such as 15 μm or less, 10 μm or less, 5 μm or less, 1 μm or less, 0.75μm or less, 0.5 μm or less, 0.4 μm or less, 0.3 μm or less, 0.2 μm orless, 0.1 μm or less, 0.01 μm or less, or 0.001 μm or less.

The microparticles may have any suitable shape, including but notlimited to spherical, spheroid, rod-shaped, disk-shaped, pyramid-shaped,cube-shaped, cylinder-shaped, nanohelical-shaped, nanospring-shaped,nanoring-shaped, arrow-shaped, teardrop-shaped, tetrapod-shaped,prism-shaped, or any other suitable geometric or non-geometric shape.

The microparticles may be made of any suitable material, including butnot limited to, latex, polystyrene, silica, a magnetic material, aparamagnetic material, or any combination thereof.

According to certain embodiments, the particles are cells and an eventis a cell event. The cells may be present in a cellular sample ofinterest, including but not limited to a blood sample (e.g., a wholeblood sample or fraction thereof), a cerebrospinal fluid sample, aperitoneal fluid sample, a pericardial fluid sample, a pleural fluidsample, a synovial fluid sample, a urine sample, a saliva sample, a tearsample, a semen sample, an amniotic fluid sample, a sputum sample, andthe like, as well as samples obtained from cysts, tumors, and the like.According to certain embodiments, a cell event is a small cell event.For example, the cell event may be a platelet event, e.g., in thevicinity of a Gaussian profile of a larger cell event, such as aGaussian profile of a white blood cell (WBC) event. Other small cellevents which may be determined by the methods of the present disclosureinclude microorganism cell events. Microorganisms of interest includebacteria, archaea, protozoa, fungi, algae and the like. According tocertain embodiments, the small cell event is a cell debris event.

An example method for detecting events in a flow cytometer, and itsutility in obtaining clean event histograms for events such as plateletevents, is provided in Example 1 of the Experimental section below.

As summarized above, the present disclosure also provides methods fordetecting cells in a flow cytometer. Such methods include flowing acellular sample including cells through a flow cell of a flow cytometer,detecting optical signals from the cells flowing through the flow cellat a first gain setting, and detecting optical signals from the cellsflowing through the flow cell at the second gain setting. The secondgain setting is different from the first gain setting.

The methods employing first and second gain settings find a variety ofuses. For example, the present inventors have found that changing thegain of the photo electronics signals during a flow cytometric assaymakes it possible to use the entire dynamic range of the analog todigital converter (ADC). This improves the signal to noise ratio (SNR)and resolution from analog to digital conversion. Optimum gain settingsof the photo electronic signals can be employed to increase the distancemetrics in the clusters and provide better differentiation.

A first particular useful example application of the present methodsthat employ at least first and second gain settings is for detectingcells in cellular samples (e.g., body fluid samples) that exhibitsubstantial variation (e.g., from 5 to 5 million) in cell counts fromsample to sample. A fixed amount of fluorescent dye is typically addedin the assay prior to injecting the cellular sample in the flow stream.This fixed amount is selected based on the median expected cell eventsin the sample. If the sample has greater than normal nucleated cells,the dye concentration may not be sufficient to stain the higher numberof cells. The resulting weak staining produces sub-optimal fluorescentsignal and the cell events signals occupy only a small portion of thedynamic range of the ADC. By changing the gain settings (e.g., the gainsettings of the photo diodes and/or photomultiplier tube) after theinitial data capture, it is possible to set the gains such that allphoto sensor signals occupy the full dynamic rage of the ADC. With thisfull ADC resolution, the present inventors have found that it is easierto differentiate between various cell types.

During the initial data period of data capture, photo sensor signals maybe collected with default gain settings. An optimal gain setting toeffectively use the complete dynamic range of the ADC may be computedwithin few milliseconds. The revised gain settings may be applied forthe remaining duration of the assay, providing better signal to noiseratio and full use of dynamic range of the ADC. An example method thatemploys first and second gain settings is provided in Example 2 of theExperimental section below.

A second particular useful example application of the present methodsthat employ at least first and second gain settings is for countingdifferent types of cells in a heterogeneous cellular sample (e.g., ablood sample) where the different cell types produce flow cytometricsignals of different intensity. By way of example, the methods find usein capturing platelet events at a first (lower) gain setting, andcapturing red blood cell (RBC) events at a second (higher) gain setting,as schematically illustrated in FIG. 9. In the example embodiment shownin FIG. 9, PLT collection occurs for a duration (1.8 seconds in thisexample) at a first gain setting, and then RBC collection subsequentlyoccurs for a duration (1.7 seconds in this example) at a second gainsetting. In this way, the assay can be performed in a first “mode”(e.g., a PLT mode) and then switched to a second “mode” (e.g., an RBCmode) in which the first and second modes differ according to the gainsettings. A PLT mode may be run to capture details of PLT cell events,such as low end of PLT concentration, platelet distribution width (PDW)and/or mean platelet volume (MPV). Once the PLT data is captured, thegain settings are optimally lowered for collection of RBC cell eventssuch that RBC cells occupy the full dynamic range of the ADC. While theRBC data is being captured, the PLT concentration and cell count resultis available. When the blood sample exhibits a very low end of PLTcount, the gain settings may be set back (increased) to capture more PLTcell events as may be necessary for statistical relevance.

Accordingly, aspects of the methods that employ at least first andsecond gain settings include analyzing the optical signals detected atthe first gain setting to detect a first cell type, and analyzing theoptical signals detected at the second gain setting to detect a secondcell type.

When practicing the present methods employing first and second gainsettings, the second gain setting may be greater than the first gainsetting, e.g., when it is determined that the cell event signal strengthis less than optimal (e.g., to account for a cellular sample having ahigher than normal cell concentration). In certain aspects, the secondgain setting may be less than the first gain setting, e.g., when it isdetermined that the cell event signal strength is greater than optimal(e.g., to account for a cellular sample having a lower than normal cellconcentration).

According to certain embodiments, the first and second gain settingsindependently include a photo diodes gain setting, a photo multipliertubes (PMT) gain setting, or both.

It will be understood that while the methods include first and secondgain settings, the methods may employ a plurality of gain settings asmay be desired for optimal collection of various cell events (e.g.,corresponding to various cell types, etc.). For example, 2 or more, 3 ormore, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more,or 10 or more different gain settings may be employed during an assayfor optimal collection of various cell events.

The detection of optical signals at the first gain setting may be forthe same duration as detection of optical signals at the second gainsetting. In other aspects, detecting optical signals at the first gainsetting is for a different duration than detecting optical signals atthe second gain setting. According to certain embodiments, detectingoptical signals at the first and second gain settings is for a durationindependently selected from 0.1 to 10 seconds (e.g., 0.5 to 5 seconds).

In certain aspects, a flow cytometric assay is run for an initialduration at the first gain setting, and then run for the remainingduration (which may be longer than the initial duration) of the assay atan improved (e.g., optimal) second gain setting. The improved gainsetting may be determined based on signals (e.g., signal strength)collected during the initial duration at the first gain setting.

As summarized above, the present disclosure also provides methods fordetermining a level of contamination in a flow cell. By “contaminationin a flow cell” or “flow cell contamination” is meant deposits (e.g., ofcellular debris, protein, and/or the like) on the inside wall of theflow cell. When these deposits occur, the optical path through theflowcell becomes clouded, distorting the laser beam and changing theintensity of the various scattered/fluorescent light signals.

Such methods include flowing a sample including particles through a flowcell, collecting raw particle event data during the flowing, andcounting the number of particle events within the raw particle eventdata. The methods further include filtering the raw particle event datawith an inverse Gaussian filter coefficient to produce a filteredsignal, where the inverse Gaussian filter coefficient is based on anexpected power spectrum variance. The methods further includedetermining the energy of the filtered signal (which is proportional tothe contamination in the flowcell and number of cell events in the rawparticle event data), and subtracting a clean flow cell baseline energyfrom the determined energy of the filtered signal, to determine a levelof contamination in the flow cell. According to certain embodiments,subsequent to collecting raw particle event data during the flowing, theraw data values below threshold are replaced with zero. In certainaspects, the value resulting from subtracting the clean flow cellbaseline energy from the determined energy of the filtered signal isscaled based on the number of events.

The methods find use in determining a level of contamination in a flowcell. According to certain embodiments, an application of the methods isto detect an early stage of flow cell contamination. A feature of themethods is that they permit detection of flow cell contamination suchthat the need to run a standard (e.g., a standard reference particle(SRP)) in a flow cytometer to detect flow cell contamination isobviated.

A contaminated flow cell generates more than one reflection andtherefore smears, overlaps with, and adds distortion to a particle(e.g., cell) event response. The added signal arising from flow cellcontamination distorts the original signal and generates a higherfrequency component within the cell event spectrum. The presentinventors have found that early stages of flow cell contamination may bedetected by analyzing the particle event response in the frequencydomain and assessing the energy of the high frequency component.

Referring to the formula above characterizing the Gaussian waveformgenerated by a cell event response, parameter μ depends upon thebaseline fluctuations. Baseline fluctuations may originate from externalinterference, laser noise, dark current and/or imperfections in thesystem electronics. A baseline restore (BLR) circuit maintains thebaseline at DC. Parameter p may be brought to zero by eliminating the DCoffset either by an analog BLR circuit or subtracting DC offset in thedigital domain. Because parameter μ can be brought to zero and varianceis constant for the time-domain distribution for all the particleevents, it is possible to transform this time-domain signal to theconstant frequency domain spectrum. The frequency domain characteristicof the signal shows the same spectrum for all cell size-related cellevents. It is therefore possible to exploit this characteristic to avoidrunning standard reference particles (SRP).

Accurate determination of a change in energy of the high frequencyspectrum due to flow cell contamination is facilitated by removal of theenergy in the spectrum accumulated from the clean flow-cell. Accordingto the present methods, the inverse Gaussian Filter or High PassFrequency Filter is computed, thereby inversing the power spectrum forthe clean flow cell event. Since the complete waveform may be capturedat high resolution (e.g., 16-bit and 10 MSPS), it is viable to digitallyfilter out the energy that is Gaussian in nature. Either finite impulseresponse (FIR) or infinite impulse response (IIR) filters in analog ordigital form can be implemented to filter out this energy.

According to certain embodiments, a digital FIR filter is employed toremove the energy contributed from the clean flow-cell. FIR filterimplementation may be carried out according to the following equation:

y[n]=b ₀ x[n]+b ₁ x[n−1]+ . . . +b _(N) x[n−N]

The equation for FIR filter implementation in the digital domain has b₀b₁ b₂ b₃ . . . b_(n) coefficients which are computed from the inverse ofthe spectrum, and x(n) is the Digitized Raw Data Signal. Y(n) is theDigital output signal of the filter. The coefficients (b₀ b₁ b₂ b₃ . . .b_(n)) can be modified further to remove the exact energy contributed bythe clean cell event.

By removing the normal (clean) energy from the signal, the extra energyof multiple reflections and distortion of the signal may be computed.The energy measurement of the high frequency component indicates theorder of contamination in the flow cell.

According to certain embodiments, counting the number of particle eventsincludes extracting raw data of a fixed time interval from an analog todigital converter, determining a DC offset, and replacing the raw datathat is below a fixed threshold with zeroes. In certain aspects, thethreshold is from 1% to 5% of full dynamic range, or the threshold is1/e2 of peak amplitude.

In certain aspects, the sample is flowed into a stream of sheath fluidwithin the flow cell, and the expected power spectrum variance is basedon the pressure of the sheath fluid. According to certain embodiments,the particles are cells. For example, the sample may be a blood sample,and the particles are blood cells.

A flow chart of a method for determining a level of contamination in aflow cell according to one embodiment of the present disclosure isprovided in FIG. 12. In this example, raw data of a fixed time intervalis extracted from the analog to digital converter (ADC). A DC offset iscomputed. The raw data below a fixed threshold (e.g., 3% of full dynamicrange or 1/e2 of peak amplitude) is replaced with zeroes. The number ofcell events with the raw data is computed. The raw data is then filteredwith pre-determined coefficients, and then the energy of the filteredsignal is computed. Baseline energy for a clean flow cell is removedbased on the number of cell events. The remaining energy may be scaledto determine the level of contamination.

An example method for determining a level of contamination in a flowcell is described in Example 3 of the Experimental section below.

When the flow cell starts becoming contaminated, feature calculations(e.g., peak height, width and higher moments) of cell events start tobecome invalid, which in turn produces erroneous reports, e.g., bloodreports. According to existing methods, it is not possible to detecterroneous blood results until the flow cell is cleaned at scheduledintervals or a diagnostic mode involving standard reference particles(SRPs) is run. The methods of the present disclosure make it possible todetect the early stage of flow cell contamination, thereby enabling theavoidance of erroneous blood results and obviating the need foroperating the flow cytometer in a special mode involving expensive SRPbeads. In addition, discarded samples are minimized on account of themethods enabling detection of very early stages of flow cellcontamination. Further, the system does not need to be cleaned (e.g.,bleached) more frequently than required, thereby increasing the life ofthe tubing in the system.

Computer-Readable Media and Systems

As summarized above, also provided by the present disclosure arecomputer-readable media and systems, e.g., which find use in practicingthe methods of the present disclosure.

Non-transitory computer readable media of the present disclosureinclude, but are not limited to, disks (e.g., magnetic or opticaldisks), solid-state storage drives, cards, tapes, drums, punched cards,barcodes, and magnetic ink characters and other medium that may be usedfor storing representations, instructions, and/or the like.

In certain aspects, provided are non-transitory computer-readable mediathat find use in practicing the methods for detecting events in a flowcytometer described hereinabove. According to certain embodiments, suchnon-transitory computer-readable media include instructions that, whenexecuted by a computing device (e.g., a computing device of a flowcytometer), cause the computing device to extract putative eventfeatures from particles flowing through a flow cell, time-stamp putativeevents, determine a time difference between a putative previous eventand a putative current event, and compare the time difference to athreshold duration. If the time difference is greater than the thresholdduration, the instructions cause the computing device to store theputative current event as a current event. If the time difference isless than the threshold duration, the instructions cause the computingdevice to compare a peak height feature of the putative current eventand a peak height feature of the putative previous event to a thresholdpeak height. If the peak height feature of the putative current event isless than the threshold peak height, the instructions cause thecomputing device to discard the putative current event. If the peakheight feature of the putative previous event is less than the thresholdpeak height, the instructions cause the computing device to discard theputative previous event. If the peak height feature of the putativeprevious event is greater than the threshold peak height, theinstructions cause the computing device to store the putative previousevent as a previous event. In certain aspects, an event is distinguishedfrom a signal selected from optical system side-lobes, fluidics drift,baseline drift, flow cell contamination, and combinations thereof.According to certain embodiments, the particles are cells, such that anevent is a cell event. Cell events of interest include, e.g., small cellevents. Small cell events may be platelet events, a microorganism cellevent, a cell debris event, and/or the like.

In certain aspects, provided are non-transitory computer-readable mediathat find use in practicing the above-described methods for detectingcells in a flow cytometer. According to certain embodiments, suchnon-transitory computer-readable media include instructions that, whenexecuted by a computing device (e.g., a computing device of a flowcytometer), cause the computing device to detect optical signals fromcells of a cellular sample flowing through a flow cell at a first gainsetting, change the gain setting from the first gain setting to a secondgain setting, and detect optical signals from cells flowing through theflow cell at the second gain setting. The second gain setting isdifferent from the first gain setting. For example, the second gainsetting may be greater than the first gain setting (e.g., to account forthe cellular sample having a high cell concentration, or to detect celltypes that typically exhibit a weaker signal intensity). In otheraspects, the second gain setting is less than the first gain setting(e.g., to account for the cellular sample having a low cellconcentration, or to detect cell types that typically exhibit a strongsignal intensity). The first and second gain settings may include aphoto diodes gain setting, a photo multiplier tubes (PMT) gain setting,or both. According to certain embodiments, the first gain setting ishigher than the second gain setting, and platelets are detected at thefirst gain setting and red blood cells (RBCs) are detected at the secondgain setting. Detecting optical signals at the first gain setting may befor the same or a different duration as detecting optical signals at thesecond gain setting. In certain aspects, detecting optical signals atthe first and second gain settings is for a duration independentlyselected from 0.1 to 10 seconds (e.g., 0.5 to 5 seconds).

In certain aspects, provided are non-transitory computer-readable mediathat find use in practicing the above-described methods for determininga level of contamination in a flow cell. According to certainembodiments, such non-transitory computer-readable media includeinstructions that, when executed by a computing device (e.g., acomputing device of a flow cytometer), cause the computing device tocollect raw particle event data as a sample including particles isflowed through a flow cell, count the number of particle events withinthe raw particle event data, and filter the raw particle event data withan inverse Gaussian filter coefficient to produce a filtered signal. Theinverse Gaussian filter coefficient is based on an expected powerspectrum variance. The instructions further cause the computing deviceto determine the energy of the filtered signal, and subtract a cleanflow cell baseline energy from the determined energy of the filteredsignal, to determine a level of contamination in the flow cell. Incertain aspects, counting the number of particle events includesextracting raw data of a fixed time interval from an analog to digitalconverter, determining a DC offset, and replacing the raw data that isbelow a fixed threshold with zeroes. According to certain embodiments,the threshold is from 1% to 5% of full dynamic range, or the thresholdis 1/e² of peak amplitude. In certain aspects, the expected powerspectrum variance is based on the pressure of the sheath fluid.According to certain embodiments, the particles are cells. For example,the cells may be cells of a blood sample.

Also provided by the present disclosure are systems (e.g., flowcytometry systems, which may be a subsystem of an automated hematologysystem) adapted to perform any of the methods of the present disclosure.Such systems may include any of the above-described non-transitorycomputer-readable media.

In certain aspects, a system of the present disclosure is a flowcytometer. Such a system includes a flow cell, an excitation sourcepositioned to excite particles of a sample of interest (e.g., a bloodsample) flowing through the flow cell, and one or more detectors fordetecting optical signals emitted from the excited particles. An exampleof a flow cytometer which may include any of the above-describednon-transitory computer-readable media and suitable for practicing themethods of the present disclosure is schematically illustrated in FIG.25. Flow cytometer 10 includes a source of light 12, a front mirror 14and a rear mirror 16 for beam bending, a beam expander module 18containing a first cylindrical lens 20 and a second cylindrical lens 22,a focusing lens 24, a fine beam adjuster 26, a flow cell 28, a forwardscatter lens 30, a bulls-eye detector 32, a first photomultiplier tube34, a second photomultiplier tube 36, and a third photomultiplier tube38. The bulls-eye detector 32 has an inner detector 32 a for 0° lightscatter and an outer detector 32 b for 7° light scatter.

In certain aspects, the source of light is a laser. However, othersources of light can be used, such as, for example, lamps (e.g.,mercury, xenon). The source of light 12 can be a vertically polarizedair-cooled Coherent Cube laser, commercially available from Coherent,Inc., Santa Clara, Calif. Lasers having wavelengths ranging from 350 nmto 700 nm can be used. Operating conditions for the laser aresubstantially similar to those of lasers currently used with “CELL-DYN”automated hematology analyzers.

Additional details relating to the flow cell, the lenses, the focusinglens, the fine-beam adjust mechanism and the laser focusing lens can befound in U.S. Pat. No. 5,631,165, incorporated herein by reference,particularly at column 41, line 32 through column 43, line 11. Theforward optical path system shown in FIG. 2 includes a sphericalplano-convex lens 30 and a two-element photo-diode detector 32 locatedin the back focal plane of the lens. In this configuration, each pointwithin the two-element photodiode detector 32 maps to a specificcollection angle of light from cells moving through the flow cell 28.The detector 32 can be a bulls-eye detector capable of detecting axiallight loss (ALL) and intermediate angle forward scatter (IAS). U.S. Pat.No. 5,631,165 describes various alternatives to this detector at column43, lines 12-52.

A first photomultiplier tube 34 (PMT1) measures depolarized side scatter(DSS). The second photomultiplier tube 36 (PMT2) measures polarized sidescatter (PSS), and the third photomultiplier tube 38 (PMT3) measuresfluorescence emission from 440 nm to 680 nm, depending upon thefluorescent dye selected and the source of light employed. Thephotomultiplier tube collects fluorescent signals in a broad range ofwavelengths in order to increase the strength of the signal.Side-scatter and fluorescent emissions are directed to thesephotomultiplier tubes by dichroic beam splitters 40 and 42, whichtransmit and reflect efficiently at the required wavelengths to enableefficient detection. U.S. Pat. No. 5,631,165 describes variousadditional details relating to the photomultiplier tubes at column 43,line 53 though column 44, line 4.

Sensitivity is enhanced at photomultiplier tubes 34, 36, and 38, whenmeasuring fluorescence, by using an immersion collection system. Theimmersion collection system is one that optically couples the first lens30 to the flow cell 28 by means of a refractive index matching layer,enabling collection of light over a wide angle. U.S. Pat. No. 5,631,165describes various additional details of this optical system at column44, lines 5-31.

The condenser 44 is an optical lens system with aberration correctionsufficient for diffraction limited imaging used in high resolutionmicroscopy. U.S. Pat. No. 5,631,165 describes various additional detailsof this optical system at column 44, lines 32-60.

The functions of other components shown in FIG. 25, i.e., a slit 46, afield lens 48, and a second slit 50, are described in U.S. Pat. No.5,631,165, at column 44, line 63 through column 45, line 26. Opticalfilters 52 or 56 and a polarizer 52 or 56, which are inserted into thelight paths of the photomultiplier tubes to change the wavelength or thepolarization or both the wavelength and the polarization of the detectedlight, are also described in U.S. Pat. No. 5,631,165, at column 44, line63 through column 45, line 26. Optical filters that are suitable for useherein include band-pass filters and long-pass filters.

The photomultiplier tubes 34, 36, and 38 detect either side-scatter(light scattered in a cone whose axis is approximately perpendicular tothe incident laser beam) or fluorescence (light emitted from the cellsat a different wavelength from that of the incident laser beam).

While select portions of U.S. Pat. No. 5,631,165 are referenced above,U.S. Pat. No. 5,631,165 is incorporated herein by reference in itsentirety. According to certain embodiments, a flow cytometer of thepresent disclosure employs an Avalanche Photodiode (APD) as thephotosensor.

The following examples are offered by way of illustration and not by wayof limitation.

EXPERIMENTAL Example 1: Removal of Undesirable Cell Event Signals forEvent Detection

In this example of event detection, the particles are cells, andundesirable cell event signals are removed to obtain a clean platelet(PLT) cell histogram.

A flow chart for the method used in this example is shown in FIG. 4.Samples are acquired from an analog to digital converter (ADC) at 20million samples per second resolution on the trigger channel. Followingsample acquisition and subtraction of the baseline restore (BLR) value,cell event features are extracted and time-stamped. If the pulse widthis greater than 3 μs, the current cell event is discarded. If the pulsewidth is less than 3 μs, the time difference between the previous andcurrent event is computed. If the time difference between the previousand current cell event is greater than 2 μs, the current event is savedand the time-stamp extracted. If the peak height feature of the currentevent is less than 2× the threshold, the current event is discarded. Ifthe peak height feature of the current event is not less than 2× thethreshold and the peak height feature of the previous event is less than2× the threshold, the previous event is discarded. If the peak heightfeature of the current event is not less than 2× the threshold and thepeak height feature of the previous event is not less than 2× thethreshold, the previous event and time-stamp are written to a list modefile. The above process is carried out until the data collection periodexpires.

The utility of this example method for obtaining clean platelet (PLT)cell histograms is demonstrated in FIGS. 5-8. FIG. 5 shows thecontamination of the PLT population at closer to the origin on theAmplitude axis. FIG. 7 shows the contamination of the PLT populationvery clearly in the PSS side scattered channel. In FIG. 7, invalidevents are shown clearly in different color. Those events are not PLTcell events but invalid events generated by optical artifact, APDthermal effect, Fluidics drift or Electronics baseline noise. FIG. 6 isthe output of this algorithm to achieve clean log normal distribution ofthe PLT cell population. In this example, the sample was diluted by1:250 and the injection rate was 2.9 μL/s.

Example 2: Improved Capture of Cell Events by Dynamic Adjustment ofOptical Gains

In this example, a digital data capture subsystem keeps track of cellevent counts as well as the duration of the assay. At a predeterminedtime in the assay, if the data capture system determines that certaincell event signals (e.g., forward and scatter light from the cellevents) are below an expected value, the variable gain settings of thephoto electronics signals are increased. If the data capture subsystemfinds many saturated cell events compared to unsaturated events in thefixed amount of time (˜300 ms) at the beginning of an assay, thevariable gain settings of the photo electronics signals are decreased.

Plots of cell events in the absence of dynamic gain adjustment are shownin FIG. 10. Plots of cell events in which dynamic gain adjustment wasemployed during the assay are shown in FIG. 11. In FIGS. 10 and 11, theupper and lower plots are PSS/ALL and PSS/DSS, respectively.

FIGS. 10 and 11 demonstrate the beneficial effect of dynamic gainadjustment, especially in body fluid mode or any assay where the dyeamount for the assay is fixed based on the average cell countexpectation. This method addresses the problem of dye penetrationvariation in the nucleus of the cells, especially when there issignificantly more or less amount of nucleated cells in the assay thanexpected. FIG. 10 shows the effect when there are more cells thanexpected in the assay. Since all the cells are not stained, it is veryhard to differentiate the cell population as the cluster distancebetween the cell population is very small. FIG. 11 shows that the methoddescribed here involving variable gain adjustment facilitates separatingthe cell population clusters in the scatter plot. FIG. 11 shows wherethe nucleated cells are more than expected and the fixed amount of dyewas insufficient to stain all the nucleated cells. Conversely, whenthere are much fewer nucleated cells than expected, the fixed amount ofdye stains the nucleus and plasma and in turn, saturates the cell eventresponse on the photo sensors. Saturated signals do not carry anyadditional information for cluster separation and hence it is verydesirable to reduce the gain dynamically while the assay is active.

An additional advantage of this method is to separate one assay in twoor more pre-determined gain settings. FIG. 9 shows the RBC assay isdivided into two groups to achieve better extraction of features of thePLT cell population. It shows that small cell events can be extracted atlarger gain settings and larger cells can be extracted at lesser gainsettings. This way it is possible to take advantage of complete dynamicrange of the analog to digital converter electronics.

The decision can be made dynamically and during the assay runtime andgain can be adjusted and stabilized within tens of milliseconds withoutaffecting data integrity.

Example 3: Detection of Early-Stage Flow Cell Contamination by AnalyzingCell Event Responses in the Frequency Domain and the Energy of the HighFrequency Component

The theoretically ideal Gaussian pulse of 2 microsecond duration at 10MSPS occupies 1.75 MHz bandwidth. Instead of using the theoreticalvalue, the bandwidth was computed from experimental data taken on aclean flow cell with existing electronics. Experimental data shows thatthe interested bandwidth in the clean flow cell is approximately 1.65MHz and is therefore very close to the theoretical one. The powerspectrum of the ideal Gaussian pulse and the power spectrum ofexperimental data taken from a clean flow cell are shown in FIGS. 13 and14, respectively.

A Gaussian pulse represents a clean cell event signal. Contamination inthe flow cell adds more energy in the cell event signal. The inverseresponse of the Gaussian spectrum is computed in the frequency domain tosubtract the energy of the cell event that represents the clean flowcell. The remaining energy in the cell event is directly proportional tothe contamination in the flow cell. Effective normalization of thespectrum depends on the system noise floor. When the noise floor issufficiently low (e.g., less than 1% of the full dynamic range), thepower spectrum may be readily normalized to the cell amplitude.

From the raw data, the number of cell events is counted based on thegated peaks. Once the peaks are identified, the raw data is firstfloored to threshold value to eliminate baseline noise. Then raw data ofonly cell events is filtered with the inverse Gaussian filter. A graphof the frequency response of the inverse Gaussian filter is depicted inFIG. 15.

Output of the inverse Gaussian filter indicates any energy from flowcell contamination. In order to precisely measure the impact of thecontamination, residual energy for clean flow cell events aresubtracted. The final value is an excellent indication of remainingenergy due to multiple reflections from the flow cell.

Data obtained from a highly-contaminated flow cell is shown in FIGS.16-18. FIG. 16 shows the time-domain signal for a highly-contaminatedflow cell, in which baseline noise was removed from the raw data from anADC (stage 1). FIG. 17 shows the frequency domain characteristic of thehighly-contaminated flow cell (power spectrum of the raw data) (stage2). FIG. 18 shows the power spectrum of the raw data after removing theenergy equivalent to a clean flow cell. In this example, the high energy(−8 dB/Hz) indicates a high level of contamination.

Data obtained from a flow cell at an early stage of contamination isshown in FIGS. 19-21. FIG. 19 shows the time-domain signal for a flowcell at an early stage of contamination, in which baseline noise wasremoved from the raw data from an ADC. FIG. 20 shows the frequencydomain characteristic of the flow cell at an early stage ofcontamination (power spectrum of the raw data). FIG. 21 shows the powerspectrum of the raw data after removing the energy equivalent to a cleanflow cell. In this example, the lower energy (−24 dB/Hz) indicates anearly stage of contamination.

Data obtained from a flow cell that is not contaminated is shown inFIGS. 22-24. FIG. 22 shows the time-domain signal for a flow cell thatis not contaminated, in which baseline noise was removed from the rawdata from an ADC. FIG. 23 shows the frequency domain characteristic ofthe flow cell that is not contaminated (power spectrum of the raw data).FIG. 24 shows the power spectrum of the raw data after removing theenergy equivalent to a clean flow cell. The low energy (−32 dB/Hz)indicates that the flow cell is not contaminated.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it is readily apparent to those of ordinary skill in theart in light of the teachings of this invention that certain changes andmodifications may be made thereto without departing from the spirit orscope of the appended claims.

Accordingly, the preceding merely illustrates the principles of theinvention. It will be appreciated that those skilled in the art will beable to devise various arrangements which, although not explicitlydescribed or shown herein, embody the principles of the invention andare included within its spirit and scope. Furthermore, all examples andconditional language recited herein are principally intended to aid thereader in understanding the principles of the invention and the conceptscontributed by the inventors to furthering the art, and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the invention as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents and equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure. The scope of the presentinvention, therefore, is not intended to be limited to the exemplaryembodiments shown and described herein. Rather, the scope and spirit ofpresent invention is embodied by the appended claims.

1-37. (canceled)
 38. A flow cytometer, comprising: a flow cell; anoptical detection system optically coupled to the flow cell; and anon-transitory computer readable medium storing instructions that, whenexecuted by a computing device, cause the computing device to: detectoptical signals from cells of a cellular sample flowing through the flowcell at a first gain setting; change the gain setting from the firstgain setting to a second gain setting; and using the optical detectionsystem, detect optical signals from cells flowing through the flow cellat the second gain setting.
 39. The flow cytometer of claim 38, whereinthe second gain setting is greater than the first gain setting.
 40. Theflow cytometer of claim 39, wherein the second gain setting is toaccount for the cellular sample having a high cell concentration. 41.The flow cytometer of claim 38, wherein the second gain setting is lessthan the first gain setting.
 42. The flow cytometer of claim 41, whereinthe second gain setting is to account for the cellular sample having alow cell concentration.
 43. The flow cytometer of claim 38, wherein thefirst and second gain settings comprise a photo diodes gain setting, aphoto multiplier tubes (PMT) gain setting, or both.
 44. The flowcytometer of claim 38, wherein the cellular sample is a blood sample.45. The flow cytometer of claim 44, wherein the first gain setting ishigher than the second gain setting, and wherein platelets are detectedat the first gain setting and red blood cells (RBCs) are detected at thesecond gain setting.
 46. The flow cytometer of claim 38, whereindetecting optical signals at the first gain setting is for the sameduration as detecting optical signals at the second gain setting. 47.The flow cytometer of claim 38, wherein detecting optical signals at thefirst gain setting is for a different duration than detecting opticalsignals at the second gain setting.
 48. The flow cytometer of claim 38,wherein detecting optical signals at the first and second gain settingsis for a duration independently selected from 0.1 to 10 seconds.
 49. Theflow cytometer of claim 38, wherein detecting optical signals at thefirst and second gain settings is for a duration independently selectedfrom 0.5 to 5 seconds. 50.-65. (canceled)